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SUMMARY:Linear Estimation of Structural and Causal Effects for Nonseparabl
 e Panel Data - Whitney K Newey (Massachusetts Institute of Technology)
DTSTART:20260127T143000Z
DTEND:20260127T153000Z
UID:TALK241564@talks.cam.ac.uk
DESCRIPTION:This paper develops linear estimators for structural and causa
 l parameters in nonparametric\,nonseparable models using panel data. These
  models incorporate unobserved\, time-varying\, individual heterogeneity\,
  which may be correlated with the regressors. Estimation is based on an ap
 proximation of the nonseparable model by a linear sieve specification with
  individual specific parameters. Effects of interest are estimated by a bi
 as corrected average of individual ridge regressions. We demonstrate how t
 his approach can be applied to estimate causal effects\, counterfactual co
 nsumer welfare\, and averages of individual taxable income elasticities. W
 e show that the proposed estimator has an empirical Bayes interpretation a
 nd possesses a number of other useful properties. We formulate Large-T asy
 mptotics that can accommodate discrete regressors and which bypass partial
  identification in this case. We employ the methods to estimate average eq
 uivalent variation and deadweight loss for potential price increases using
  data on grocery purchases. This paper is coauthored with V. Chernozhukov\
 , B. Deaner\, Y. Gao\, and J.A. Hausman
LOCATION:Seminar Room 1\, Newton Institute
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